Generally conventional vehicle system prognosis is performed using knowledge-driven methods, estimation methods, time-series analysis based methods, and machine learning base methods. These methods are applied to sensor data values obtained from one or more sensors onboard, for example, a vehicle. The knowledge-driven methods, for example, rely on domain knowledge in order to place rales and/or constraints on the sensor data values. The knowledge-driven methods may generally detect more subtle faults in systems of the vehicle than the estimation methods, time-series analysis based methods, and machine learning base methods. However, because the knowledge-driven methods are not data-driven, the knowledge-driven methods are generally less robust (e.g., exhibit a greater number of false negatives) than the estimation methods, time-series analysis based methods, and machine learning base methods.
The estimation methods generally utilize the sensor data from unique, but correlated sensors in order to detect system faults. While the estimation methods do not necessarily require completely redundant sensors, the estimation methods do require that a significantly strong correlation exist between two different sensors at the very least and that the fault does not exist far enough downstream from the sensors such that the sets of recorded measurements from both sensors are affected.
Time-series analysis based methods generally make use of temporal correlations among current and past measurements from a single sensor in order to predict future measurements. The time-series analysis based methods are generally more robust than pure knowledge-driven methods because the time-series analysis based methods can capture unknown system failure signatures in their learned parameters; however, time-series analysis based methods are generally less robust than machine learning approaches due to their reliance on a fixed, pre-defined model.
Machine learning based approaches generally infer a model of normal versus abnormal sensor measurements using training data, and then statistically detect and identify classes of faults. The machine learning based approaches are generally the most robust of the aforementioned vehicle system prognosis methods because the machine learning based approaches are purely data-driven. However, machine learning based approaches also require the most data to train and tend to be less capable of detecting failures that induce subtle changes in the sensor signals.